ABSTRACT
The Covid-19 pandemic has highlighted an era in hearing health care that necessitates a comprehensive rethinking of audiology service delivery. There has been a significant increase in the number of individuals with hearing loss who seek information online. An estimated 430 million individuals worldwide suffer from hearing loss, including 11 million in the United Kingdom. The objective of this study was to identify NHS audiology service social media posts and understand how they were used to communicate service changes within audiology departments at the onset of the Covid-19 pandemic.Facebook and Twitter posts relating to audiology were extracted over a six week period (March 23 to April 30 2020) from the United Kingdom. We manually filtered the posts to remove those not directly linked to NHS audiology service communication. The extracted data was then geospatially mapped, and themes of interest were identified via a manual review. We also calculated interactions (likes, shares, comments) per post to determine the posts efficacy. A total of 981 Facebook and 291 Twitter posts were initially mined using our keywords, and following filtration, 174 posts related to NHS audiology change of service were included for analysis. The results were then analysed geographically, along with an assessment of the interactions within the included posts. NHS Trusts and Boards should consider incorporating and promoting social media to communicate service changes. Users would be notified of service modifications in real-time, and different modalities could be used (e.g. videos), resulting in a more efficient service.
Subject(s)
COVID-19 , Hearing LossABSTRACT
Background and Aim Coronary involvement in Kawasaki Disease (KD), whether its after SARS-CoV2 infection or not, can result in significant complications. There is the risk of aneurysm formation associated with inflammation and an unremitting fever. We wished to study the Vasoactive Endothelial Growth Factor (VEGF) and Heat Shock Response from a gene-expression perspective. Thereby aiming to furnish to insights that might be useful in the treatment of Kawasaki Disease. Method KD datasets based on previous work, were selected including microarray studies KD1 (GSE63881), KD2 (GSE73461), KD3 (GSE68004) and the RNAseq dataset KD4 (GSE64486) from the NCBI online repository. Based on clinical literature. HSP genes shown to be associated with angiogenesis were chosen for analysis as well as gene expression for VEFGA and VEGFB. Further in order to gain an impression of inflammatory patterns, gene expression for NFKB1 and TNF were also chosen. Tools for analysis included Gene Set Expression Analysis (GSEA). A KEGG pathway, outlining a relationship between VEGF and endothelial migration and proliferation was assumed. Results A KD dataset showed increased VEGFA and decreased VEGFB in acute versus convalescent samples. In all three KD datasets, HSPA1A and HSBAP1 genes were upregulated in acute versus convalescent samples. In KD4, cases of KD versus controls, VEGFB was down-regulated (p = 4.932e-02) and HSPBAP1 up-regulated (p = 1.202e-03). GSEA of KD1, KD2 and KD3, using Hallmark gene sets, suggested an inflammatory response with TNFA signaling via NFKB, IL6 JAK STAT 3 signaling, apoptosis, angiogenesis, and unfolded protein response. Conclusions A novel application of a model of VEGF and HSP to KD was presented. Coronary pathogenesis based on VEGF and HSP was explored. The ability to follow angiogenesis at the molecular level using a VEGF-HSP model may have therapeutic implications. Further, the significance of gene expression between VEGFA, VEGFB in KD and the relationship of HSP gene expression to angiogenesis in KD requires further study.
Subject(s)
Mucocutaneous Lymph Node Syndrome , Fever , Coronary Aneurysm , Inflammation , AneurysmABSTRACT
BackgroundThe nature and extent of persistent neuropsychiatric symptoms after COVID-19 are not established. To help inform mental health service planning in the pandemic recovery phase, we systematically determined the prevalence of neuropsychiatric symptoms in survivors of COVID-19. MethodsFor this pre-registered systematic review and meta-analysis (PROSPERO ID CRD42021239750) we searched PubMed, EMBASE, CINAHL and PsycINFO to 20th February 2021, plus our own curated database. We included peer-reviewed studies reporting neuropsychiatric symptoms at post-acute or later time-points after COVID-19 infection, and in control groups where available. For each study a minimum of two authors extracted summary data. For each symptom we calculated a primary pooled prevalence using generalised linear mixed models. Heterogeneity was measured with I2. Subgroup analyses were conducted for COVID-19 hospitalisation, severity, and duration of follow-up. FindingsFrom 2,844 unique titles we included 51 studies (n=18,917 patients). The mean duration of follow-up after COVID-19 was 77 days (range 14-182 days). Study quality was generally moderate. The most frequent neuropsychiatric symptom was sleep disturbance (pooled prevalence=27{middle dot}4% [95%CI 21{middle dot}4- 34{middle dot}4%]), followed by fatigue (24{middle dot}4% [17{middle dot}5-32{middle dot}9%]), objective cognitive impairment (20{middle dot}2% [10{middle dot}3-35{middle dot}7%]), anxiety (19{middle dot}1%[13{middle dot}3-26{middle dot}8%]), and post-traumatic stress (15{middle dot}7% [9{middle dot}9-24{middle dot}1%]). Only two studies reported symptoms in control groups, both reporting higher frequencies in Covid-19 survivors versus controls. Between-study heterogeneity was high (I2=79{middle dot}6%-98{middle dot}6%). There was little or no evidence of differential symptom prevalence based on hospitalisation status, severity, or follow-up duration. InterpretationNeuropsychiatric symptoms are common and persistent after recovery from COVID-19. The literature on longer-term consequences is still maturing, but indicates a particularly high frequency of insomnia, fatigue, cognitive impairment, and anxiety disorders in the first six months after infection. FundingJPR is supported by the Wellcome Trust (102186/B/13/Z). IK is funded through the NIHR (Oxford Health Biomedical Research Facility, Development and Skills Enhancement Award) and the Medical Research Council (Dementias Platform UK and Deep and Frequent Phenotyping study project grants). HH is funded by the German Research Foundation (DFG, Grant: HO 1286/16-1). The funders played no role in the design, analysis or decision to publish. RESEARCH IN CONTEXTO_ST_ABSEvidence before this studyC_ST_ABSNeuropsychiatric symptoms like cognitive impairment, fatigue, insomnia, depression and anxiety can be highly disabling. Recently there has been increasing awareness of persistent neuropsychiatric symptoms after COVID-19 infection, but a systematic synthesis of these symptoms is not available. In this review we searched five databases up to 20th February 2021, to establish the pooled prevalence of individual neuropsychiatric symptoms up to six months after COVID-19. Added value of this studyThis study establishes which of a range of neuropsychiatric symptoms are the most common after COVID-19. We found high rates in general, with little convincing evidence that these symptoms lessen in frequency during the follow-up periods studied. ImplicationsPersistent neuropsychiatric symptoms are common and appear to be limited neither to the post-acute phase, nor to recovery only from severe COVID-19. Our results imply that health services should plan for high rates of requirement for multidisciplinary services (including neurological, neuropsychiatric and psychological management) as populations recover from the COVID-19 pandemic.
Subject(s)
COVID-19ABSTRACT
ObjectivesThere is accumulating evidence of the neurological and neuropsychiatric features of infection with SARS-CoV-2. In this systematic review and meta-analysis, we aimed to describe the characteristics of the early literature and estimate point prevalences for neurological and neuropsychiatric manifestations. MethodsWe searched MEDLINE, Embase, PsycInfo and CINAHL up to 18 July 2020 for randomised controlled trials, cohort studies, case-control studies, cross-sectional studies and case series. Studies reporting prevalences of neurological or neuropsychiatric symptoms were synthesised into meta-analyses to estimate pooled prevalence. Results13,292 records were screened by at least two authors to identify 215 included studies, of which there were 37 cohort studies, 15 case-control studies, 80 cross-sectional studies and 83 case series from 30 countries. 147 studies were included in the meta-analysis. The symptoms with the highest prevalence were anosmia (43.1% [35.2--51.3], n=15,975, 63 studies), weakness (40.0% [27.9--53.5], n=221, 3 studies), fatigue (37.8% [31.6--44.4], n=21,101, 67 studies), dysgeusia (37.2% [30.0--45.3], n=13,686, 52 studies), myalgia (25.1% [19.8--31.3], n=66.268, 76 studies), depression (23.0 % [11.8--40.2], n=43,128, 10 studies), headache (20.7% [95% CI 16.1--26.1], n=64,613, 84 studies), anxiety (15.9% [5.6--37.7], n=42,566, 9 studies) and altered mental status (8.2% [4.4--14.8], n=49,326, 19 studies). Heterogeneity for most clinical manifestations was high. ConclusionsNeurological and neuropsychiatric symptoms of COVID-19 in the pandemics early phase are varied and common. The neurological and psychiatric academic communities should develop systems to facilitate high-quality methodologies, including more rapid examination of the longitudinal course of neuropsychiatric complications of newly emerging diseases and their relationship to neuroimaging and inflammatory biomarkers.
Subject(s)
COVID-19ABSTRACT
BackgroundGlobal efforts towards the development and deployment of a vaccine for SARS-CoV-2 are rapidly advancing. We developed and applied an artificial-intelligence (AI)-based approach to analyse social-media public sentiment in the UK and the US towards COVID-19 vaccinations, to understand public attitude and identify topics of concern. MethodsOver 300,000 social-media posts related to COVID-19 vaccinations were extracted, including 23,571 Facebook-posts from the UK and 144,864 from the US, along with 40,268 tweets from the UK and 98,385 from the US respectively, from 1st March - 22nd November 2020. We used natural language processing and deep learning based techniques to predict average sentiments, sentiment trends and topics of discussion. These were analysed longitudinally and geo-spatially, and a manual reading of randomly selected posts around points of interest helped identify underlying themes and validated insights from the analysis. ResultsWe found overall averaged positive, negative and neutral sentiment in the UK to be 58%, 22% and 17%, compared to 56%, 24% and 18% in the US, respectively. Public optimism over vaccine development, effectiveness and trials as well as concerns over safety, economic viability and corporation control were identified. We compared our findings to national surveys in both countries and found them to correlate broadly. ConclusionsAI-enabled social-media analysis should be considered for adoption by institutions and governments, alongside surveys and other conventional methods of assessing public attitude. This could enable real-time assessment, at scale, of public confidence and trust in COVID-19 vaccinations, help address concerns of vaccine-sceptics and develop more effective policies and communication strategies to maximise uptake.